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The Past, Present, and Future of Effective Thought Leader Identification and Engagement
A conversation at the 2022 KOL Engagement Summit presentation between leaders of IQVIA's Thought Leader Network Center of Excellence
Aug 15, 2022
IQVIA’s approach to Thought Leader Network (TLN) identification relies on a comprehensive assessment of primary and secondary data sources that offer a detailed understanding of individual leader profiles, skills, and preferences. The profile data allows for precise targeting utilizing proprietary scoring, tiering, and segmentation that matches each identified thought leader to their naturally occurring healthcare provider (HCP) professional peer networks. Insights provided by this integrated, comprehensive analysis support customized engagement planning for a range of opportunities to achieve desired outcomes.
In a conversation between heads of IQVIA’s Thought Leader Network Center of Excellence, Darin DeCarlo and Bruce West, this blog takes a look back at the early days of thought leadership in healthcare, addresses current roles and strategies, and considers the continuing evolution of the thought leader role and its potential impact on peer education, behaviors, and new product adoption.
Darin DeCarlo (DD): Bruce, the way in which the healthcare industry identifies and prioritizes Key Opinion Leaders (KOL) has evolved quite a bit over time. What has been your experience with respect to this evolution? And what’s led to some of these changes?Bruce West (BW): Historically, the targeting focus for Medical Science Liaison (MSL) deployment has been centered on the classic ‘KOL’ profile. In other words, those scientific/academic disease category experts who publish, speak, conduct clinical trials, and are broadly recognized as the “experts” within a defined disease category. These classic KOLs typically are associated with recognized scientific and academic medical centers of excellence, such as Johns Hopkins and Duke.
MSLs have been focused and deployed to this top echelon of thought leaders, and in many companies, this traditional approach for MSL deployment continues to this day, but not in all companies. The catalyst for change over the past decade has largely been driven by a deeper appreciation of what constitutes a ’Thought Leader’, and importantly, the recognition that thought leaders – even if differing in profile and focus, exist at all levels in the marketplace (national, regional, and local). All of these different thought leaders play important roles in advancing peer education, shaping behavior, and facilitating the adoption of new products.
DD: So, it’s gotten more complex over time. What has driven the need to look at this in a more comprehensive way?BW: With expanded awareness that thought leaders exist at all levels in the marketplace, the science of identifying thought leaders in a disease market has expanded significantly. Today, best practice in thought leader identification integrates multiple, complementary sources of data – derived from a combination of secondary, primary, and AI/ML research approaches – to identify, profile, and map the peer relationship connections for the full range of thought leaders in disease-specific markets. Thought leaders differ substantially in terms of their skills and interests, as well as scope of geographic impact in the marketplace. The data assembled to comprehensively identify the full range of thought leaders includes profiling, segmentation, scoring, and matching identified thought leaders to specific engagement opportunities.
DD: Can you share a bit more on the concept of ‘network science’ and why it is important?BW: Our perspective at IQVIA is focused not only on determining who the thought leaders are, but also to understand how each identified thought leader is connected in a network sense to their peer physicians who are managing patients. We do this work at a disease level to offer clients specific insight that improves all aspects of their medical and commercial targeting. We view each disease market as a connected network of peers, linked by mutual trust, shared professional work, and often friendship. Consequently, our interest in thought leaders does not end with identification and the generation of a list of leaders, but actually only begins there. Our true interest is in understanding how these identified leaders connect in the broader network of HCPs, providing opinion, leadership, and education that serves to disseminate information and learning about new products and science.
DD: Tell us more about ‘hidden leaders’ and their role in facilitating learning and product adoption.BW: Identification of the traditional KOL (the recognized scientific/academic leaders within a disease category) is the easy part. These individuals can rapidly be identified via a combination of secondary data sources that are largely available to the public. The more challenging research objective is associated with identifying the top regional and local clinical leaders who are trusted and impact the exchange of knowledge and product adoption outside of the academic setting. These leaders we describe as the hidden leaders. They play a critical role in bridging knowledge and behavior change from the academic to the clinical setting. They act as both translators (of science to clinical practice) and teachers for their network-connected peer HCPs.
DD: How do you combine all of this information in order to make it actionable?BW: It starts with that recognition that thought leaders differ substantially in their individual profiles, skills, and interests. Therefore, understanding these individual distinctions is critical for effective engagement planning. For example, identifying a thought leader who is appropriate for a Scientific Ad Board is quite different than identifying a thought leader who would be appropriate for a regional commercial speaker bureau.
By integrating the various data – both secondary and primary – we are able to weight and filter the aggregate database to customize our selection of appropriate thought leaders for specific engagement opportunities (i.e., use cases). There is a level of “art” associated with this exercise, and it certainly takes a high level of experience to understand which leader profile characteristics are most desirable for a range of medical and commercial engagement types. This customized engagement planning process results in far more effective engagements, and ultimately, competitive advantage for those able to plan with this level of precision.
DD: There’s a lot of talk now of predictive analytics, artificial intelligence (AI), and machine learning (ML) both on the medical and commercial sides. How is it now being used in KOL identification and network mapping?BW: At IQVIA, we are very excited about the application of AI/ML analytics to extend our understanding of both thought leadership and HCP networks. In the past, we were able to demonstrate that primary, peer nomination, and network research generates unique insights that differ substantially from network analysis that is dependent on referral (claims) data. The challenge historically with our primary, peer nomination approach is that our response rates were limited (8-15%). The insights and leaders identified, especially those hidden leaders at the clinical level were always extremely valuable, but we continued to want to complete our understanding of the full network.
By combining the learnings from our primary peer nomination research, with IQVIA’s Big Data factory of claims and Rx data, as well as detailed profile information (e.g., affiliation, subspecialty, location, etc.). Machine Learning has allowed us to develop algorithms that accurately predict the most likely leader for each target HCP in a full market, taking us from an understanding of 8-15% of HCP/Leader connections to virtually the entire market.
We’re very excited about this development and believe it has significant strategic implications for the pharma/biotech industry in both medical and commercial areas, impacting initiatives ranging from effective MSL deployment to sales targeting, omnichannel strategy, speaker bureau and event planning, and many other important activities.
DD: Lastly, Bruce, how have we evolved as an industry in terms of leveraging technology in analyzing KOL data and optimizing engagements with them?BW: We view thought leader (TL) engagement planning and coordination as effective blocking and tackling, but it is often an area where the majority of companies in the industry could substantially improve. Here at IQVIA, we have the distinct advantage of having line of sight into the operations of many different companies, and in most, the topic of coordinated thought leader engagement planning is a recognized pain point. The reasons for an approach lacking in proactive, coordinated, and defined planning are many. They range from perceptions of the limitations associated with regulatory and/or compliance adherence and the “firewalls” that exist between medical and commercial functions, or from the role of an external agency to the lack of records and performance metrics associated with TL Engagement planning.
IQVIA’s belief is that companies have an obligation to their thought leader customers to have an organized, coordinated, and strategic approach to TL engagement. We further believe that technology in the form of a central thought leader engagement platform is essential for proactive, coordinated planning and tracking. The net result of an approach that lacks coordination and supporting technology is a lack of understanding of your performance in the market, an over-dependence on a selected few thought leaders, and an external impression among thought leaders that the company is neither well-organized nor effectively coordinated internally. IQVIA provides a solution with an integrated, proactive planning tool where TL engagement at the individual and collective levels can be encouraged, tracked, and reported to management.
Effective Thought Leader Identification and Engagement
Implementing strategies for identifying and engaging thought leaders